Visualizing Gridded Datasets with Large Number of Missing Values

Abstract:

Much of the research in scientific visualization has focused on
complete sets of gridded data.
This paper presents our experience dealing with gridded data
sets with large number of missing or invalid data,
and some of our experiments in addressing the shortcomings
of standard off-the-shelf visualization algorithms.
In particular,
we discuss the options in modifying known algorithms to
adjust to the specifics of sparse datasets,
and provide a new technique to smooth out the
side-effects of the operations.
We apply our findings to data acquired from NEXRAD (NEXt generation
RADars) weather radars,
which usually have no more than 3 to 4 percent
of all possible cell points filled.

Paper:

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